Many implanted cardiac devices monitor the electrical activity of the heart for tachyarrhythmias. When the implanted device senses a possible tachyarrhythmia, a processor in the implanted device performs computations to classify the tachyarrhythmia. Classifying the tachyarrhythmia may include identifying or discriminating the form of tachyarrhythmia, such as non-sustained ventricular tachycardia, sustained ventricular tachycardia or dual-chamber tachycardia. Some forms of tachyarrhythmia are life-threatening, while other forms of tachyarrhythmia pose little risk to the life of the patient. When the implanted device identifies a life-threatening tachyarrhythmia, the implanted device may provide therapy to the heart, such as anti-tachycardia pacing, cardioversion or defibrillation.
Classifying the tachyarrhythmia involves applying powerful discriminatory algorithms, and consequently involves extensive computation by the processor. Classification computations take more time than many other processor operations, and therefore consume more energy than many other processor operations. As a result, classification computations generally act as a greater drain on the battery of the implanted device, in comparison to other processor operations.
As discriminatory algorithms to classify tachyarrhythmias have become more sophisticated, the computation effort associated with classification has increased. More sophisticated classification algorithms require more computation and drain more energy from the battery.
To prolong battery life, the implanted device performs classification computations in a defined set of circumstances. When the patient's heart rate is normal, for example, the processor does not need to classify any arrhythmia, and so the processor avoids performing energy-demanding classification computations. The processor may operate in a low-energy state when the patient's heart rate is normal, performing minimal, short-duration computations with each cardiac cycle, thereby consuming less energy. The processor departs from the low-energy state and makes a transition to a high-energy computational state when conditions exist that call for the energy-demanding computations.
In a conventional implanted cardiac device, the transition from the low-energy state to the high-energy computational state is based upon heart rate. In other words, the device monitors the rate of activation of the ventricles and/or atria, and makes a transition to the high-energy computational state when the rate exceeds a predetermined threshold for a predetermined duration. The threshold rate and duration are typically set by the patient's physician. Equivalently, the physician may set a threshold time interval between activations, which is inversely proportional to the threshold rate. One such threshold interval is defined as the tachycardia detection interval (TDI). The threshold duration may be set as a programmed number of intervals to detection (NID). Rhythms with intervals less than the TDI for at least NID consecutive beats trigger entry to the high-energy computational state. The discussion that follows will refer to intervals and to rates, and is equally applicable to both.
With conventional implanted cardiac devices, when the patient experiences a tachyarrhythmia with intervals greater than the TDI, the device does not enter the high-energy computational state and consequently does not classify the arrhythmia. The device could fail to classify a tachyarrhythmia, such as ventricular tachycardia, if the intervals are greater than the TDI. Ventricular tachycardia may be a serious, and possibly life-threatening, event.
As a practical matter, tachyarrhythmia classification is a prerequisite to effective treatment. When a tachyarrhythmia is not classified, the device typically will not provide therapy to treat the tachyarrhythmia. Unfortunately, some tachyarrhythmias that fail to cross the threshold can be life-threatening and yet may go untreated.
The patient's physician may choose to detect and treat slower tachyarrhythmias by setting a more easily crossed threshold. Not all slower tachyarrhythmias are dangerous to the patient, however, and setting a more easily crossed threshold may cause the processor to spend more time in the high-energy computational state performing classification computations. Increased time in the high-energy computational state in turn results in greater demand on the batteries in the device. The implanted device may therefore spend considerable energy to classify rhythms, many of which are not dangerous.
As a result, a rate-based transition may comprise unattractive trade-offs. Setting a TDI too high may lead to wasted computational effort that unnecessarily drains the batteries in the implanted device. The relationship between the TDI and the resulting computational demand is approximately exponential, so increasing the TDI by a small amount may result in a significant increase in computational activity and battery drain. Setting the TDI too low, however, may result in potentially dangerous tachyarrhythmias being unclassified and untreated.